Demo: fMRI surface-based searchlights with LDA classifier
The data used here is available from http://cosmomvpa.org/datadb.zip
This example uses the following dataset: + 'digit' A participant made finger pressed with the index and middle finger of the right hand during 4 runs in an fMRI study. Each run was divided in 4 blocks with presses of each finger and analyzed with the GLM, resulting in 2*4*4=32 t-values
The example shows four possible searchlight analyses, covering typical use cases: 1) single or twin surfaces + Caret and BrainVoyager use a single surface; a parameter 'offsets' is used to define which voxels are considered to be in the "grey matter" (but this may be not so precise) + FreeSurfer uses twin surfaces (pial and white), and voxels in between or on them are considered to be in the grey matter 2) lower resolution output map + in the canonical surface-based searchlight, each node on the input surface(s) is assigned a measure value (accuracy, in this example) + it is also possible to have output in a lower resolution version than the input surfaces; this reduces both the execution time (a Good Thing) and spatial precision (a Bad Thing). Two approaches are illustrated to use a lower resolution surface for output: 1) from MapIcosahedron, with a lower value for the number of divisions of the triangles 2) using a surface subsampling approach, implemented by surfing_subsample_surface
In all cases a searchlight is run with a 100 voxel searchlight, using a disc for which the metric radius varies from node to node. For a fixed metric radius of the disc, use a positive value for 'radius' below. Distances are measured across the cortical surface using a geodesic distance metric.
This example requires the surfing toolbox, github.com/nno/surfing
This example may take quite some time to run. For faster execution, set ld=16 (instead of ld=64) below
If you use this code for a publication, please cite: Oosterhof, N.N., Wiestler, T, Downing, P.E., & Diedrichsen, J. (2011) A comparison of volume-based and surface-based information mapping. Neuroimage. DOI:10.1016/j.neuroimage.2010.04.270
- For CoSMoMVPA's copyright information and license terms, #
- see the COPYING file distributed with CoSMoMVPA. #
Contents
Check externals
cosmo_check_external('surfing');
Set data paths
The function cosmo_config() returns a struct containing paths to tutorial data. (Alternatively the paths can be set manually without using cosmo_config.)
config=cosmo_config(); digit_study_path=fullfile(config.tutorial_data_path,'digit'); readme_fn=fullfile(digit_study_path,'README'); cosmo_type(readme_fn); output_path=config.output_data_path; % reset citation list cosmo_check_external('-tic'); % resolution parameter for input surfaces % 64 is for high-quality results; use 16 for fast execution ld=64; % Twin surface case (FS) pial_fn=fullfile(digit_study_path,... sprintf('ico%d_mh.pial_al.asc', ld)); white_fn=fullfile(digit_study_path,... sprintf('ico%d_mh.smoothwm_al.asc', ld)); % Single surface case (Caret/BV) intermediate_fn=fullfile(digit_study_path,... sprintf('ico%d_mh.intermediate_al.asc', ld)); % Used for visualization inflated_fn=fullfile(digit_study_path,... sprintf('ico%d_mh.inflated_alCoMmedial.asc', ld));
Overview
--------
fMRI responses to a human participant pressing buttons with the index and middle finger.
Contents
--------
- glm_T_stats_allruns+orig.{BRIK,HEAD}:
t-statistics associated with finger presses.
There are 4 runs, each with 4 blocks.
Each block has two t-statistics, one for each of button presses for the
index and middle finger (in that order).
- epi+orig.{HEAD,BRIK}:
A single EPI image.
- icoXX_mh.YY.asc:
Surface anatomy meshes of two hemispheres with standard topology, generated
using AFNI SUMA's MapIcosahedron. Left and right hemisphere surfaces were
merged in the order left, right; the first half of the nodes and faces refer
to the left hemisphere, the second half to the right hemisphere.
XX={16,64} is the number of linear divisions of MapIcosahedron; surfaces
have (XX^2)*10+2 nodes and XX^2*20 faces in each hemisphere.
YY={pial_al,white_al} are the outer and inner surfaces around the grey matter
generated by FreeSurfer. YY=intermediate_al is the node-wise average of the
pial and white surfaces; this surface can be used for single-surface
analyses when surfaces are generated using Caret or BrainVoyager
YY=inflated_alCoMmedial are inflated surfaces with the center of mass
along the medial side of the two hemispheres. These surfaces are suitable
for visualization purposes.
Methods
-------
This dataset contains data from a fingerpress experiment where a participant
(28y right-handed male) pressed buttons on a box with the index and middle
finger of the right hand while fMRI volumes were acquired. Over four runs, in
total sixteen blocks of 72s finger presses were acquired; in each block the
participant pressed the index and middle finger during different `mini-blocks'
of 8 s each. Each block was preceded and followed by a 16s rest period.
Functional data was preprocessed in AFNI with despiking, time slice correction,
motion correction, and scaling to percent signal change by dividing the signal
for each volume by the mean of the run. No spatial smoothing or interpolation
was applied to the functional data, except for interpolation during motion
correction. The preprocessed data was analyzed with a general linear model
(GLM) with separate regressors for each finger and each block (and some
regressors of no interest) to obtain t-values for each finger in each block.
License
-------
The contents are made available by Nikolaas N. Oosterhof <nikolaas.oosterhof
|at| unitn.it> under the Creative Commons CC0 1.0 Universal Public Domain
Dedication ("CC0"). See the LICENSE file for details, or visit
http://creativecommons.org/publicdomain/zero/1.0/deed.en.
Contact
-------
Nikolaas N. Oosterhof <nikolaas.oosterhof |at| unitn.it>
Set parameters
% Searchlight radius: select 100 features in each searchlight % (to use a fixed radius of 8mm, use: % cosmo_surficial_neighborhood(...,'radius',8) % in the code below) feature_count=100; % Single surface case: select voxels that are 3 mm or closer to the surface % on the white-matter side, up to voxels that are 2 mm from the surface on % the pial matter side single_surf_offsets=[-2 3]; % Single surface case: number of iterations to downsample surface lowres_output_onesurf_niter=10; % Twin surface case: number of linear divisions from MapIcosahedron lowres_output_twosurf_icold=16; lowres_intermediate_fn=fullfile(digit_study_path,... sprintf('ico%d_mh.intermediate_al.asc',... lowres_output_twosurf_icold)); % Use the cosmo_cross_validation_measure and set its parameters % (classifier and partitions) in a measure_args struct. measure = @cosmo_crossvalidation_measure; measure_args = struct(); % Define which classifier to use, using a function handle. % Alternatives are @cosmo_classify_{svm,nn,naive_bayes} measure_args.classifier = @cosmo_classify_lda;
Load functional data
data_path=digit_study_path; data_fn=fullfile(data_path,'glm_T_stats_perblock+orig'); targets=repmat(1:2,1,16)'; % class labels: 1 2 1 2 1 2 1 2 1 2 ... 1 2 chunks=floor(((1:32)-1)/8)+1; % run labels: 1 1 1 1 1 1 1 1 2 2 ... 4 4 ds = cosmo_fmri_dataset(data_fn,'targets',targets,'chunks',chunks); % remove zero elements zero_msk=all(ds.samples==0,1); ds = cosmo_slice(ds, ~zero_msk, 2); fprintf('Dataset has %d samples and %d features\n', size(ds.samples)); % print dataset fprintf('Dataset input:\n'); cosmo_disp(ds);
Dataset has 32 samples and 168097 features
Dataset input:
.sa
.labels
{ 'fi_i_R1_B01#0_Tstat'
'fi_m_R1_B01#0_Tstat'
'fi_i_R1_B02#0_Tstat'
:
'fi_m_R4_B03#0_Tstat'
'fi_i_R4_B04#0_Tstat'
'fi_m_R4_B04#0_Tstat' }@32x1
.stats
{ 'Ttest(168)'
'Ttest(168)'
'Ttest(168)'
:
'Ttest(168)'
'Ttest(168)'
'Ttest(168)' }@32x1
.targets
[ 1
2
1
:
2
1
2 ]@32x1
.chunks
[ 1
1
1
:
4
4
4 ]@32x1
.a
.vol
.mat
[ -2.5 0 0 71.2
0 -2.5 0 112
0 0 2.5 43
0 0 0 1 ]
.dim
[ 61 84 41 ]
.xform
'scanner_anat'
.fdim
.labels
{ 'i'
'j'
'k' }
.values
{ [ 1 2 3 ... 59 60 61 ]@1x61
[ 1 2 3 ... 82 83 84 ]@1x84
[ 1 2 3 ... 39 40 41 ]@1x41 }
.samples
[ 0 0 0 ... -0.878 0.178 4.66
0 0 0 ... 2.48 -0.3 1.12
0 0 0 ... -1.05 -0.707 1.04
: : : : : :
-2.21 -0.243 -0.377 ... -1.59 1 0.987
-1.97 1.06 0.673 ... -1.23 1.41 0.999
-4.5 1.16 1.53 ... 1.86 -1.31 0.405 ]@32x168097
.fa
.i
[ 1 2 3 ... 59 60 61 ]@1x168097
.j
[ 1 1 1 ... 84 84 84 ]@1x168097
.k
[ 3 3 3 ... 36 36 36 ]@1x168097
Set partition scheme. odd_even is fast; for publication-quality analysis
nfold_partitioner is recommended. Alternatives are: - cosmo_nfold_partitioner (take-one-chunk-out crossvalidation) - cosmo_nchoosek_partitioner (take-K-chunks-out " ").
measure_args.partitions = cosmo_oddeven_partitioner(ds); % print measure and arguments fprintf('Searchlight measure:\n'); cosmo_disp(measure); fprintf('Searchlight measure arguments:\n'); cosmo_disp(measure_args);
Searchlight measure:
@cosmo_c ... measure
Searchlight measure arguments:
.classifier
@cosmo_classify_lda
.partitions
.train_indices
{ [ 1 [ 9
2 10
3 11
: :
22 30
23 31
24 ]@16x1 32 ]@16x1 }
.test_indices
{ [ 9 [ 1
10 2
11 3
: :
30 22
31 23
32 ]@16x1 24 ]@16x1 }
Read inflated surface
[v_inf,f_inf]=surfing_read(inflated_fn); fprintf('The inflated surface has %d vertices, %d faces\n',... size(v_inf,1), size(f_inf,1))
The inflated surface has 81924 vertices, 163840 faces
Run four types of searchlights
for one_surf=[true,false] if one_surf desc='1surf'; else desc='2surfs'; end for lowres_output=[false,true] if lowres_output desc=sprintf('%s_lowres', desc); end fprintf('\n\n *** Starting analysis with %s *** \n\n\n', desc) % define searchlight surface paramters for each type of analysis if one_surf && lowres_output % single surface (Caret/BV) with lower-res output surf_def={intermediate_fn,single_surf_offsets,... lowres_output_onesurf_niter}; elseif one_surf && ~lowres_output % single surface (Caret/BV) with original-res output surf_def={intermediate_fn,single_surf_offsets}; elseif ~one_surf && lowres_output % single surface (FS) with lower-res output surf_def={white_fn,pial_fn,lowres_intermediate_fn}; elseif ~one_surf && ~lowres_output % single surface (FS) with original-res output surf_def={white_fn,pial_fn}; else assert(false); % should never get here end % Define the feature neighborhood for each node on the surface % - nbrhood has the neighborhood information % - vo and fo are vertices and faces of the output surface % - out2in is the mapping from output to input surface fprintf('Defining neighborhood with %s\n', desc); [nbrhood,vo,fo,out2in]=cosmo_surficial_neighborhood(ds,surf_def,... 'count',feature_count); % print neighborhood fprintf('Searchlight neighborhood definition:\n'); cosmo_disp(nbrhood); fprintf('The output surface has %d vertices, %d nodes\n',... size(vo,1), size(fo,1)); % Run the searchlight lda_results = cosmo_searchlight(ds,nbrhood,measure,measure_args); % print searchlight output fprintf('Dataset output:\n'); cosmo_disp(lda_results); % Apply the node mapping from the surifical neighborhood % to the high-res inflated surface. % (This example shows how such a mapping can be applied to new % surfaces) if lowres_output v_inf_out=v_inf(out2in,:); f_inf_out=fo; else v_inf_out=v_inf; f_inf_out=f_inf; end % visualize the surfaces, if the afni matlab toolbox is present if cosmo_check_external('afni',false) nvertices=size(v_inf_out,1); opt=struct(); for show_edge=[false, true] opt.ShowEdge=show_edge; if show_edge t='with edges'; else t='without edges'; end header=strrep([desc ' ' t],'_',' '); DispIVSurf(vo,fo,1:nvertices,lda_results.samples',0,opt); title(sprintf('Original %s', header)); DispIVSurf(v_inf_out,f_inf_out,1:nvertices,... lda_results.samples',0,opt); title(sprintf('Inflated %s', header)); end else fprintf('skip surface display; no afni matlab toolbox\n'); end if lowres_output && one_surf % in this example only this case a new surface was generated. % To aid visualization using external tools, store it to disc. % The surface is stored in ASCII, GIFTI and BV SRF % formats, if the required externals are present surf_output_fn=fullfile(output_path,['inflated_' desc]); % AFNI/SUMA ASC surfing_write([surf_output_fn '.asc'],v_inf_out,f_inf_out); % BV SRF if cosmo_check_external('neuroelf',false) surfing_write([surf_output_fn '.srf'],v_inf_out,f_inf_out); end % GIFTI if cosmo_check_external('gifti',false) surfing_write([surf_output_fn '.gii'],v_inf_out,f_inf_out); end end % store searchlight results data_output_fn=fullfile(output_path,['lda_' desc]); if cosmo_check_external('afni',false) cosmo_map2surface(lda_results, [data_output_fn '.niml.dset']); end if cosmo_check_external('neuroelf',false) cosmo_map2surface(lda_results, [data_output_fn '.smp']); end % store voxel counts (how often each voxel is in a neighborhood) % take a random sample (the first one) from the input dataset % and count how often each voxel was selected. % If everything works, then voxels in the grey matter have high % voxel counts but voxels outside it low or zero counts. % Thus, this can be used as a sanity check that can be visualized % easily. vox_count_ds=cosmo_slice(ds,1); vox_count_ds.samples(:)=0; ncenters=numel(nbrhood.neighbors); for k=1:ncenters idxs=nbrhood.neighbors{k}; % feature indices in neigborhood vox_count_ds.samples(idxs)=vox_count_ds.samples(idxs)+1; end vox_count_output_fn=fullfile(output_path,['vox_count_' desc]); % store voxel count results cosmo_map2fmri(vox_count_ds, [vox_count_output_fn '.nii']); if cosmo_check_external('afni',false) cosmo_map2fmri(vox_count_ds, [vox_count_output_fn '+orig']); end end end % Show citation information cosmo_check_external('-cite');
*** Starting analysis with 1surf ***
Defining neighborhood with 1surf
Warning: found 14674 / 81924 center nodes outside the volume, these will be
ignored.
Using 168097 / 210084 voxels in functional volume mask
+00:30:43 [####################] -00:00:00 r=8.07, 100.0 vox
Searchlight neighborhood definition:
.a
.fdim
.labels
{ 'node_indices' }
.values
{ [ 1 2 3 ... 8.19e+04 8.19e+04 8.19e+04 ]@1x81924 }
.fa
.node_indices
[ 1 2 3 ... 8.19e+04 8.19e+04 8.19e+04 ]@1x81924
.neighbors
{ [ ]
[ 1.13e+05 1.13e+05 1.13e+05 ... 1.34e+05 1.34e+05 1.34e+05 ]@1x99
[ 1.05e+05 1.1e+05 1.1e+05 ... 1.35e+05 1.35e+05 1.4e+05 ]@1x99
:
[ 1.67e+04 1.68e+04 1.68e+04 ... 4.25e+04 4.25e+04 4.26e+04 ]@1x100
[ 1.68e+04 1.68e+04 1.68e+04 ... 4.25e+04 4.26e+04 4.26e+04 ]@1x100
[ 1.67e+04 1.67e+04 1.68e+04 ... 4.25e+04 4.25e+04 4.25e+04 ]@1x100 }@81924x1
.origin
.a
.vol
.mat
[ -2.5 0 0 71.2
0 -2.5 0 112
0 0 2.5 43
0 0 0 1 ]
.dim
[ 61 84 41 ]
.xform
'scanner_anat'
.fdim
.labels
{ 'i'
'j'
'k' }
.values
{ [ 1 2 3 ... 59 60 61 ]@1x61
[ 1 2 3 ... 82 83 84 ]@1x84
[ 1 2 3 ... 39 40 41 ]@1x41 }
.fa
.i
[ 1 2 3 ... 59 60 61 ]@1x168097
.j
[ 1 1 1 ... 84 84 84 ]@1x168097
.k
[ 3 3 3 ... 36 36 36 ]@1x168097
The output surface has 81924 vertices, 163840 nodes
+00:02:34 [####################] -00:00:00
Dataset output:
.a
.fdim
.labels
{ 'node_indices' }
.values
{ [ 1 2 3 ... 8.19e+04 8.19e+04 8.19e+04 ]@1x81924 }
.fa
.node_indices
[ 1 2 3 ... 8.19e+04 8.19e+04 8.19e+04 ]@1x81924
.center_ids
[ 1 2 3 ... 8.19e+04 8.19e+04 8.19e+04 ]@1x81924
.samples
[ 0.5 0.562 0.719 ... 0.594 0.625 0.656 ]@1x81924
.sa
.labels
{ 'accuracy' }
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to none
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to none
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to black
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to black
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
*** Starting analysis with 1surf_lowres ***
Defining neighborhood with 1surf_lowres
+00:00:03 [####################] -00:00:00 100.0% simple nodes
+00:00:02 [####################] -00:00:00 81924 nodes: 16.6% removed
+00:00:02 [####################] -00:00:00 100.0% simple nodes
+00:00:02 [####################] -00:00:00 68312 nodes: 17.1% removed
+00:00:02 [####################] -00:00:00 100.0% simple nodes
+00:00:01 [####################] -00:00:00 56620 nodes: 15.3% removed
+00:00:01 [####################] -00:00:00 100.0% simple nodes
+00:00:01 [####################] -00:00:00 47949 nodes: 16.7% removed
+00:00:01 [####################] -00:00:00 100.0% simple nodes
+00:00:01 [####################] -00:00:00 39921 nodes: 16.1% removed
+00:00:01 [####################] -00:00:00 100.0% simple nodes
+00:00:01 [####################] -00:00:00 33486 nodes: 16.0% removed
+00:00:01 [####################] -00:00:00 100.0% simple nodes
+00:00:00 [####################] -00:00:00 28127 nodes: 15.6% removed
+00:00:01 [####################] -00:00:00 100.0% simple nodes
+00:00:00 [####################] -00:00:00 23746 nodes: 15.8% removed
+00:00:01 [####################] -00:00:00 100.0% simple nodes
+00:00:00 [####################] -00:00:00 19995 nodes: 15.6% removed
+00:00:00 [####################] -00:00:00 100.0% simple nodes
+00:00:00 [####################] -00:00:00 16872 nodes: 15.5% removed
+00:00:10 [####################] -00:00:00 81924 -> 14257 nodes
Warning: found 2572 / 14257 center nodes outside the volume, these will be
ignored.
Using 168097 / 210084 voxels in functional volume mask
+00:01:30 [####################] -00:00:00 r=8.08, 100.0 vox
Searchlight neighborhood definition:
.a
.fdim
.labels
{ 'node_indices' }
.values
{ [ 1 2 3 ... 1.43e+04 1.43e+04 1.43e+04 ]@1x14257 }
.fa
.node_indices
[ 1 2 3 ... 1.43e+04 1.43e+04 1.43e+04 ]@1x14257
.neighbors
{ [ 904 905 965 ... 2.02e+04 2.03e+04 2.03e+04 ]@1x100
[ 1.03e+03 1.03e+03 1.03e+03 ... 2.53e+04 2.53e+04 2.53e+04 ]@1x100
[ ]
:
[ 2.19e+04 2.19e+04 2.2e+04 ... 4.26e+04 4.27e+04 4.78e+04 ]@1x101
[ 1.68e+04 1.69e+04 1.69e+04 ... 4.25e+04 4.26e+04 4.26e+04 ]@1x100
[ 1.68e+04 1.68e+04 1.68e+04 ... 4.25e+04 4.26e+04 4.26e+04 ]@1x100 }@14257x1
.origin
.a
.vol
.mat
[ -2.5 0 0 71.2
0 -2.5 0 112
0 0 2.5 43
0 0 0 1 ]
.dim
[ 61 84 41 ]
.xform
'scanner_anat'
.fdim
.labels
{ 'i'
'j'
'k' }
.values
{ [ 1 2 3 ... 59 60 61 ]@1x61
[ 1 2 3 ... 82 83 84 ]@1x84
[ 1 2 3 ... 39 40 41 ]@1x41 }
.fa
.i
[ 1 2 3 ... 59 60 61 ]@1x168097
.j
[ 1 1 1 ... 84 84 84 ]@1x168097
.k
[ 3 3 3 ... 36 36 36 ]@1x168097
The output surface has 14257 vertices, 28506 nodes
+00:00:43 [####################] -00:00:00
Dataset output:
.a
.fdim
.labels
{ 'node_indices' }
.values
{ [ 1 2 3 ... 1.43e+04 1.43e+04 1.43e+04 ]@1x14257 }
.fa
.node_indices
[ 1 2 3 ... 1.43e+04 1.43e+04 1.43e+04 ]@1x14257
.center_ids
[ 1 2 3 ... 1.43e+04 1.43e+04 1.43e+04 ]@1x14257
.samples
[ 0.5 0.5 0.5 ... 0.594 0.625 0.625 ]@1x14257
.sa
.labels
{ 'accuracy' }
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to none
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to none
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to black
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to black
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
*** Starting analysis with 2surfs ***
Defining neighborhood with 2surfs
Warning: found 14674 / 81924 center nodes outside the volume, these will be
ignored.
Using 168097 / 210084 voxels in functional volume mask
+00:13:23 [####################] -00:00:00 r=10.11, 100.0 vox
Searchlight neighborhood definition:
.a
.fdim
.labels
{ 'node_indices' }
.values
{ [ 1 2 3 ... 8.19e+04 8.19e+04 8.19e+04 ]@1x81924 }
.fa
.node_indices
[ 1 2 3 ... 8.19e+04 8.19e+04 8.19e+04 ]@1x81924
.neighbors
{ [ ]
[ 1.08e+05 1.08e+05 1.08e+05 ... 1.34e+05 1.34e+05 1.34e+05 ]@1x100
[ 1.05e+05 1.05e+05 1.05e+05 ... 1.4e+05 1.4e+05 1.41e+05 ]@1x100
:
[ 1.17e+04 1.17e+04 1.18e+04 ... 3.75e+04 4.26e+04 4.26e+04 ]@1x99
[ 1.67e+04 1.67e+04 1.67e+04 ... 4.26e+04 4.26e+04 4.26e+04 ]@1x100
[ 1.17e+04 1.17e+04 1.67e+04 ... 3.75e+04 4.26e+04 4.26e+04 ]@1x100 }@81924x1
.origin
.a
.vol
.mat
[ -2.5 0 0 71.2
0 -2.5 0 112
0 0 2.5 43
0 0 0 1 ]
.dim
[ 61 84 41 ]
.xform
'scanner_anat'
.fdim
.labels
{ 'i'
'j'
'k' }
.values
{ [ 1 2 3 ... 59 60 61 ]@1x61
[ 1 2 3 ... 82 83 84 ]@1x84
[ 1 2 3 ... 39 40 41 ]@1x41 }
.fa
.i
[ 1 2 3 ... 59 60 61 ]@1x168097
.j
[ 1 1 1 ... 84 84 84 ]@1x168097
.k
[ 3 3 3 ... 36 36 36 ]@1x168097
The output surface has 81924 vertices, 163840 nodes
+00:01:39 [####################] -00:00:00
Dataset output:
.a
.fdim
.labels
{ 'node_indices' }
.values
{ [ 1 2 3 ... 8.19e+04 8.19e+04 8.19e+04 ]@1x81924 }
.fa
.node_indices
[ 1 2 3 ... 8.19e+04 8.19e+04 8.19e+04 ]@1x81924
.center_ids
[ 1 2 3 ... 8.19e+04 8.19e+04 8.19e+04 ]@1x81924
.samples
[ 0.5 0.5 0.656 ... 0.656 0.594 0.625 ]@1x81924
.sa
.labels
{ 'accuracy' }
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to none
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to none
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to black
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to black
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
*** Starting analysis with 2surfs_lowres ***
Defining neighborhood with 2surfs_lowres
+00:00:03 [####################] -00:00:00 81924 -> 5124 nodes
Warning: found 911 / 5124 center nodes outside the volume, these will be
ignored.
Using 168097 / 210084 voxels in functional volume mask
+00:00:38 [####################] -00:00:00 r=10.12, 100.0 vox
Searchlight neighborhood definition:
.a
.fdim
.labels
{ 'node_indices' }
.values
{ [ 1 2 3 ... 5.12e+03 5.12e+03 5.12e+03 ]@1x5124 }
.fa
.node_indices
[ 1 2 3 ... 5.12e+03 5.12e+03 5.12e+03 ]@1x5124
.neighbors
{ [ ]
[ 1.08e+05 1.08e+05 1.08e+05 ... 1.34e+05 1.34e+05 1.34e+05 ]@1x100
[ 1.05e+05 1.05e+05 1.05e+05 ... 1.4e+05 1.4e+05 1.41e+05 ]@1x100
:
[ 1.18e+04 1.18e+04 1.18e+04 ... 3.75e+04 3.75e+04 3.76e+04 ]@1x100
[ 2.71e+04 2.71e+04 2.72e+04 ... 4.28e+04 4.28e+04 4.28e+04 ]@1x100
[ 1.68e+04 1.69e+04 1.69e+04 ... 4.26e+04 4.26e+04 4.27e+04 ]@1x100 }@5124x1
.origin
.a
.vol
.mat
[ -2.5 0 0 71.2
0 -2.5 0 112
0 0 2.5 43
0 0 0 1 ]
.dim
[ 61 84 41 ]
.xform
'scanner_anat'
.fdim
.labels
{ 'i'
'j'
'k' }
.values
{ [ 1 2 3 ... 59 60 61 ]@1x61
[ 1 2 3 ... 82 83 84 ]@1x84
[ 1 2 3 ... 39 40 41 ]@1x41 }
.fa
.i
[ 1 2 3 ... 59 60 61 ]@1x168097
.j
[ 1 1 1 ... 84 84 84 ]@1x168097
.k
[ 3 3 3 ... 36 36 36 ]@1x168097
The output surface has 5124 vertices, 10240 nodes
+00:00:06 [####################] -00:00:00
Dataset output:
.a
.fdim
.labels
{ 'node_indices' }
.values
{ [ 1 2 3 ... 5.12e+03 5.12e+03 5.12e+03 ]@1x5124 }
.fa
.node_indices
[ 1 2 3 ... 5.12e+03 5.12e+03 5.12e+03 ]@1x5124
.center_ids
[ 1 2 3 ... 5.12e+03 5.12e+03 5.12e+03 ]@1x5124
.samples
[ 0.5 0.5 0.656 ... 0.625 0.656 0.625 ]@1x5124
.sa
.labels
{ 'accuracy' }
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to none
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to none
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to black
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
DispIVSurf verbose: z-buffer mode.
DispIVSurf verbose: Scaling ITvect to fit colormap
DispIVSurf verbose: Set hold to off
DispIVSurf verbose: Drawing Patches
DispIVSurf verbose: Applied interp for Shade.
DispIVSurf verbose: Set Edgecolor to black
DispIVSurf verbose: Set view to 2D
DispIVSurf verbose: Displaying colormap
DispIVSurf verbose: Seting Axes properties
DispIVSurf verbose: Done
If you use CoSMoMVPA and/or some other toolboxes for a publication, please cite:
Z. Saad, G. Chen. AFNI Matlab library. available online from https://github.com/afni/AFNI
N. N. Oosterhof, T. Wiestler, J. Diedrichsen (2011). A comparison of volume-based and surface-based multi-voxel pattern analysis. Neuroimage 56 (2), 593-600. Surfing toolbox available online from http://github.com/nno/surfing
Gabriel Peyre. Toolbox Fast Marching - A toolbox Fast Marching and level sets computations [https://www.ceremade.dauphine.fr/~peyre/matlab/fast-marching/content.html]. toolbox fast marching [included in surfing] available online from http://github.com/nno/surfing
J. Shen. NIFTI toolbox. available online from http://www.mathworks.com/matlabcentral/fileexchange/8797-tools-for-nifti-and-analyze-image
N. N. Oosterhof, A. C. Connolly, J. V. Haxby (2016). CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging data in Matlab / GNU Octave. Frontiers in Neuroinformatics, doi:10.3389/fninf.2016.00027.. CoSMoMVPA toolbox available online from http://cosmomvpa.org
The Mathworks, Natick, MA, United States. Matlab 9.1.0.441655 (R2016b) (September 7, 2016). available online from http://www.mathworks.com
